import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
A						=			logging.get_logger(__name__)

A						=			[
    ('bert.bert', 'visual_bert'),
    ('bert.cls', 'cls'),
    ('bert.classifier', 'cls'),
    ('token_type_embeddings_visual', 'visual_token_type_embeddings'),
    ('position_embeddings_visual', 'visual_position_embeddings'),
    ('projection', 'visual_projection'),
]

A						=			[
    'nlvr2_coco_pre_trained.th',
    'nlvr2_fine_tuned.th',
    'nlvr2_pre_trained.th',
    'vcr_coco_pre_train.th',
    'vcr_fine_tune.th',
    'vcr_pre_train.th',
    'vqa_coco_pre_trained.th',
    'vqa_fine_tuned.th',
    'vqa_pre_trained.th',
]


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple							)   ->    Dict:
		_lowerCamelCase				   =  torch.load(UpperCamelCase			,       map_location='cpu'							)
		return sd


def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]			,       UpperCamelCase     :				List[Any]			,       UpperCamelCase     :				List[str]=rename_keys_prefix							)   ->    Tuple:
		_lowerCamelCase				   =  OrderedDict()
		_lowerCamelCase				   =  torch.arange(config.max_position_embeddings							).expand((1, -1)							)
		# detector_d = OrderedDict()
		for key in d:
				if "detector" in key:
						# detector_d[key.replace('detector.','')] = d[key]
						continue
				_lowerCamelCase				   =  key
				for name_pair in rename_keys_prefix:
						_lowerCamelCase				   =  new_key.replace(name_pair[0]			,       name_pair[1]							)
				_lowerCamelCase				   =  d[key]
				if key == "bert.cls.predictions.decoder.weight":
						# Old bert code didn't have `decoder.bias`, but was added separately
						_lowerCamelCase				   =  new_d['cls.predictions.bias']
		return new_d


@torch.no_grad()
def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]			,       UpperCamelCase     :				Union[str, Any]							)   ->    List[str]:
		assert (
		    checkpoint_path.split('/'							)[-1] in ACCEPTABLE_CHECKPOINTS
		), F"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""

		# Get Config
		if "pre" in checkpoint_path:
				_lowerCamelCase				   =  'pretraining'
				if "vcr" in checkpoint_path:
						_lowerCamelCase				   =  {'visual_embedding_dim': 5_12}
				elif "vqa_advanced" in checkpoint_path:
						_lowerCamelCase				   =  {'visual_embedding_dim': 20_48}
				elif "vqa" in checkpoint_path:
						_lowerCamelCase				   =  {'visual_embedding_dim': 20_48}
				elif "nlvr" in checkpoint_path:
						_lowerCamelCase				   =  {'visual_embedding_dim': 10_24}
				else:
						raise NotImplementedError(F"""No implementation found for `{checkpoint_path}`."""							)
		else:
				if "vcr" in checkpoint_path:
						_lowerCamelCase				   =  {'visual_embedding_dim': 5_12}
						_lowerCamelCase				   =  'multichoice'
				elif "vqa_advanced" in checkpoint_path:
						_lowerCamelCase				   =  {'visual_embedding_dim': 20_48}
						_lowerCamelCase				   =  'vqa_advanced'
				elif "vqa" in checkpoint_path:
						_lowerCamelCase				   =  {'visual_embedding_dim': 20_48, 'num_labels': 31_29}
						_lowerCamelCase				   =  'vqa'
				elif "nlvr" in checkpoint_path:
						_lowerCamelCase				   =  {
						    'visual_embedding_dim': 10_24,
						    'num_labels': 2,
						}
						_lowerCamelCase				   =  'nlvr'

		_lowerCamelCase				   =  VisualBertConfig(**UpperCamelCase							)

		# Load State Dict
		_lowerCamelCase				   =  load_state_dict(UpperCamelCase							)

		_lowerCamelCase				   =  get_new_dict(UpperCamelCase			,       UpperCamelCase							)

		if model_type == "pretraining":
				_lowerCamelCase				   =  VisualBertForPreTraining(UpperCamelCase							)
		elif model_type == "vqa":
				_lowerCamelCase				   =  VisualBertForQuestionAnswering(UpperCamelCase							)
		elif model_type == "nlvr":
				_lowerCamelCase				   =  VisualBertForVisualReasoning(UpperCamelCase							)
		elif model_type == "multichoice":
				_lowerCamelCase				   =  VisualBertForMultipleChoice(UpperCamelCase							)

		model.load_state_dict(UpperCamelCase							)
		# Save Checkpoints
		Path(UpperCamelCase							).mkdir(exist_ok=UpperCamelCase							)
		model.save_pretrained(UpperCamelCase							)


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
				parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
				A						=			parser.parse_args()
				convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
def 	lowerCamelCase					(	UpperCamelCase     :				int = 60_08_51_47_51_43							)   ->    int:

		try:
				_lowerCamelCase				   =  int(UpperCamelCase							)
		except (TypeError, ValueError):
				raise TypeError('Parameter n must be int or castable to int.'							)
		if n <= 0:
				raise ValueError('Parameter n must be greater than or equal to one.'							)
		_lowerCamelCase				   =  1
		_lowerCamelCase				   =  2
		while i * i <= n:
				while n % i == 0:
						_lowerCamelCase				   =  i
						n //= i
				i += 1
		if n > 1:
				_lowerCamelCase				   =  n
		return int(UpperCamelCase							)


if __name__ == "__main__":
				print(F'''{solution() = }''')